AI Security & Governance

Comprehensive security solutions protecting AI models, applications, and infrastructure against emerging threats including prompt injection, data leakage, and adversarial attacks while ensuring compliance.

Get a ConsultationLearn More
[background image] image of an innovation lab (for an ai developer tools).
AI Security & Governance

What it is

AI Security &Governance encompasses comprehensive protection for your AI ecosystem throughCisco AI Defense—a purpose-built security platform that embeds industry-leadingAI and cybersecurity technology across your AI development, deployment, and usage lifecycle. This solution provides continuous assessment of AI models, real-time runtime protection against adversarial threats, and comprehensive visibility across your AI attack surface, ensuring your organization can innovate with AI confidently while managing risk effectively.

Who it's for

  • Enterprises deploying generative AI applications requiring protection against novel attack vectors
  • Data Science and AI Teams building and deploying custom AI models and applications
  • Security Teams responsible for protectingAI infrastructure and preventing data leakage
  • Regulated Industries (healthcare, financial services, government) with strict compliance requirements
  • Organizations using third-party AI services like Amazon Bedrock, Azure OpenAI, or other LLM platforms
  • CISOs and Risk Officers managing AI-related security and compliance obligations
  • Companies developing customer-facing AI applications requiring safety and security guardrails

Problems it solves

  • AI Visibility Gaps: Discovers and tracks AI assets (models, agents, applications) across multi-cloud environments‍
  • Emerging AI Threats: Protects against prompt injection, jailbreaking, denial of service, and adversarial attacks‍
  • Data Leakage Risks: Prevents sensitive information exposure including PII, PHI, PCI data, and proprietary code‍
  • Model Vulnerabilities: Identifies security and safety weaknesses through automated algorithmic red teaming‍
  • Compliance Challenges: Ensures AI applications align with NIST AI-RMF, MITRE ATLAS, OWASP Top 10 for LLM‍
  • Safety Concerns: Blocks toxic content and harmful outputs that could damage reputation or cause harm‍
  • Shadow AI: Identifies unsanctioned AI models and applications across the enterprise‍
  • Manual Assessment Burden: Automates vulnerability testing that traditionally requires weeks of manual effort
How our solution works

Step 1 - Discovery / Assessment

  • Comprehensive inventory of AI assets across cloud environments (models, agents, applications)
  • AI traffic flow mapping to understand enterprise AI attack surface
  • Assessment of current AI security controls and gaps
  • Review of AI development and deployment practices
  • Compliance requirements analysis (NIST, MITRE ATLAS, OWASP, industry regulations)
  • Risk assessment of existingAI applications and models
  • Integration pointsidentification with existing security infrastructure
  • Stakeholder interviews withAI teams, security teams, and business owners

Step 2 - Proposal / Design

  • AI security architecture design aligned with business requirements and risk tolerance
  • Cisco AI Defense deployment strategy (cloud visibility, validation, runtime protection)
  • Integration design with existing security operations and SIEM platforms
  • Guardrail policy design for security, privacy, and safety controls
  • Model validation and testing strategy
  • Enforcement point selection based on architecture (API gateways, network inspection, SDKs)
  • Compliance framework alignment and reporting design
  • Phased implementation roadmap with quick wins and long-term strategy
  • Training and enablement plan for security and AI teams

Step 3 - Implementation / Delivery

  • Cisco AI Defense platform deployment and configuration
  • AI Cloud Visibility implementation for asset discovery and traffic analysis
  • Integration with cloud environments (AWS, Azure, GCP) and AI platforms
  • Algorithmic red teaming implementation for automated model validation
  • Runtime protection guardrails deployment across enforcement points
  • Security policy configuration for prompt injection, data privacy, and safety
  • Integration with security operations tools (Splunk, SIEM, ticketing systems)
  • Custom guardrail development for organization-specific requirements
  • Comprehensive testing across model types and use cases
  • Documentation and operational runbook development
  • Training for security teams, AI teams, and operations staff

Step 4 - Optimization / Ongoing Support / Managed Services

  • Continuous monitoring of AI security posture and violations
  • Regular model validation and vulnerability assessments
  • Guardrail tuning and optimization based on operational feedback
  • Threat intelligence updates and emerging attack pattern protection
  • Compliance reporting and audit support
  • Quarterly security reviews and risk assessments
  • Proactive threat hunting forAI-specific indicators of compromise
  • 24/7 monitoring and incident response (optional managed services)
  • Strategic planning for new AI initiatives and security requirements
  • Continuous training on evolving AI threats and best practices

What you get (deliverables)

  • AI Asset Inventory: Comprehensive visibility into all AI models, agents, and applications
  • Security Architecture: Detailed design documentation for AI security controls
  • Cisco AI Defense Platform: Fully configured cloud-based security platform
  • Automated Red Teaming: Continuous vulnerability assessment using 200+ attack techniques
  • Runtime Guardrails: Real-time protection against security, privacy, and safety violations
  • Violations Dashboard: Centralized visibility into security events and policy violations
  • Compliance Reports: Alignment documentation with NIST, MITRE ATLAS, OWASP frameworks
  • Integration with SecurityOperations:SIEM integration and alert workflows
  • Operational Procedures: Security playbooks and incident response procedures
  • Training Materials: Comprehensive training for security and AI operations teams

Benefits / Outcomes

  • Rapid Threat Detection: Identify and blockAI-specific attacks in real-time, preventing data breaches
  • Automated VulnerabilityDiscovery: Find 200+ potential security and safety risks in minutes vs. weeks
  • Data Protection: Prevent leakage of PII,PHI, PCI data, source code, and proprietary information
  • Regulatory Compliance: Demonstrate alignment with emerging AI regulations and industry frameworks
  • Reduced Risk: Minimize exposure to novelAI attack vectors and adversarial threats
  • Operational Efficiency: Automate security assessments that would require extensive manual effort
  • Continuous Protection: Adaptive guardrails that automatically update against emerging threats
  • Developer Enablement: Allow AI teams to innovate rapidly with security built-in, not bolted-on
  • Cost Avoidance: Prevent potential breaches, regulatory fines, and reputational damage
  • Competitive Advantage: Deploy AI applications confidently and faster than competitors

Engagement model / Pricing style

  • Subscription-Based: Annual or multi-year subscriptions with tiered pricing based on usage
  • Fixed-Price Implementation: Defined scope for initial deployment and configuration
  • Managed Services: Comprehensive 24/7monitoring and management with predictable monthly costs
  • Hybrid Model: Initial implementation with ongoing subscription and optional managed services

Risk-free, practitioner-delivered POV—every engagement includes executive reporting and post-assessment recommendations.